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Dashboards

Good dashboards tell a story that helps to make the right decisions at the right time.

A business dashboard assists managers and staff in keeping track of the company's key performance indicators (KPIs) and uses business intelligence to assist companies to maximise value creation by providing insights for making data-driven decisions.

Purpose​

Close gaps where losses are occuring and assist with better decision making to optimise future returns.

Dashboards exist to proactively:

Functions​

Analytical, Operational, Strategic, Tactical.

Analytical​

A dashboard that comprises a large amount of data that analysts have generated and are using to assist executives. They provide a full perspective of data to a company, with middle management being a critical component of its use.

Operational​

KPIs essential to the day-to-day operations of a business or organization. Designed to provide real-time visibility into key processes, so that managers and other stakeholders can quickly identify where corrective action is critical.

Often systemized todo lists that show where system state requires attention.

  • Rebook Clients
  • Incomplete Exams
  • Incomplete Appointments

Strategic​

A strategic dashboard is a reporting tool that uses crucial success metrics to track a company's long-term strategy. They're frequently complicated to create, have a wide-ranging impact on a company, and are primarily employed by top management.

Reviewed on a scheduled basis, you can take in what has worked well vs strategy or which staff have outperformed which should be linked to some form of bonus or reward.

For Staff motivation: Show saturation and retention comparisons against benchmark targets.

Tactical​

The analysis and monitoring of processes by mid-level management is aided by a tactical dashboard, which emphasizes the analysis. The organization then successfully monitors the achievement of the company's aim and makes analytic recommendations for future plans.

Focus​

If you take care of the downsides the upsides will take care of themselves.

What do you need to know? What decision/action is required?

  • Worst first
  • Cashflow is king

Comparison Types​

The goal of visualizing data is to make data easier to interpret. It is better to have multiple simple views than one elaborate view with too many charts. The types of data comparison options often depend on the nature of the data and the context in which they are applied.

Time Series Comparison​

Data is tracked and compared over specific intervals of time. It is useful for understanding trends, patterns, and anomalies over time.

Categorical Comparison​

Data is compared within categories. For instance, comparing sales performance across different regions or performance of different products.

Percentage Comparison​

Data is compared as a proportion of a whole. This is often represented as a percentage.

Before and After Comparison​

This is often used in experimental or test scenarios, where you compare data before and after a certain event or intervention.

Ranking Comparison​

Iitems are compared based on a ranking order, such as a leaderboard for sales, performance, etc.

Ratio Comparison​

Comparing two quantitative variables to understand their proportion. It could be a financial ratio like debt-to-equity or any other kind of ratio.

Deviation Comparison​

Examining how much data points deviate from an expected value or standard.

Correlation Comparison​

Identifying the statistical relationship between two or more variables.

Regression Comparison​

Assessing the impact of one or several variables on another variable.

Cohort Analysis​

Comparing the behavior or metrics among different cohorts over time. Cohorts are groups of users who share similar characteristics.

Cross-sectional Analysis​

Comparing different variables at a certain point in time across different groups, areas, etc.

Longitudinal Analysis​

Comparing the same variable over long periods for the same individuals.

Interaction​

How much domain knowledge and technical expertise do you need to interact with the dashboard?

System Report​

System (Canned) reports are often delivered to a large audience and formatted professionally. These reports should be short and sweet, including data that has been batch-processed and written for weekly or monthly metrics.

  • Comes right out the box with your analytics program
  • Is based on a template someone at your organization has created or acquired

Adhoc Analysis​

Generally, canned reports have limited customization. Ad-hoc analysis, on the other hand, is a more spontaneous technique.

Granularity​

How do you determine the granularity of reporting data?

Measurement​

Depends on the context of your application and your target audience.

"Select Time Span" or "View Data By", followed by options like "Day", "Week", "Quarter", "Year", etc.

Review Frequency​

Prioritise the creation of corrective action protocols from red flag triggers.

Action protocols need to be flagged for immediate attention.

Presentation​

For visualization, chose simple colors and fonts and aim for simplicity.

Requirements

  • What's the story your data is trying to deliver?
  • What theme or context does the data relate to?
  • Who will you present your results to?
  • How big is your data?
  • What is your data type?
  • How do the different elements of your data relate to each other?

UX Design

Chart Types​

Before you choose a chart type, you need to know the story behind your data, and your target audience/media. Different chart types are better suited for different kinds of comparisons. The best method of comparison will depend on the nature of the data and the research question being addressed.

Bar Chart​

When to use:

  1. Comparing parts of a bigger set of data, highlighting different categories, or showing change over time.
  2. Have long categories label — it offers more space.
  3. If you want to illustrate both positive and negative values in the dataset.

When to avoid:

  1. If you're using multiple data points
  2. If you have many categories, avoid overloading your graph
  3. Your graph shouldn't have more than 10 bars

Line Chart​

When to use:

  1. Showing trends over time
  2. Showing relationships between two variables
  3. Showing the distribution of data points

When to avoid:

  1. If you have a small dataset
  2. If you have a lot of data points
  3. If you have a lot of categories

Pie Charts​